Datasets:
Languages:
English
Multilinguality:
multilingual
Size Categories:
100M<n<1B
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
| """C4 dataset based on Common Crawl.""" | |
| import gzip | |
| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION = """\ | |
| A colossal, cleaned version of Common Crawl's web crawl corpus. | |
| Based on Common Crawl dataset: "https://commoncrawl.org". | |
| This is the processed version of Google's C4 dataset by AllenAI. | |
| """ | |
| _CITATION = """ | |
| @article{2019t5, | |
| author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, | |
| title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, | |
| journal = {arXiv e-prints}, | |
| year = {2019}, | |
| archivePrefix = {arXiv}, | |
| eprint = {1910.10683}, | |
| } | |
| """ | |
| _URL = "https://github.com/allenai/allennlp/discussions/5056" | |
| _VARIANTS = ["en", "realnewslike", "en.noblocklist", "en.noclean"] | |
| _N_SHARDS_PER_SPLIT = { | |
| "en": {"train": 1024, "validation": 8}, | |
| "realnewslike": {"train": 512, "validation": 1}, | |
| "en.noblocklist": {"train": 1024, "validation": 8}, | |
| "en.noclean": {"train": 7168, "validation": 64}, | |
| } | |
| _DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/{name}/c4-{split}.{index:05d}-of-{n_shards:05d}.json.gz" | |
| class C4(datasets.GeneratorBasedBuilder): | |
| """C4, a colossal, cleaned version of Common Crawl's web crawl corpus.""" | |
| BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| "timestamp": datasets.Value("string"), | |
| "url": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_URL, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_urls = {} | |
| for split in ["train", "validation"]: | |
| n_shards = _N_SHARDS_PER_SPLIT[self.config.name][split] | |
| data_urls[split] = [ | |
| _DATA_URL.format(name=self.config.name, split=split, index=index, n_shards=n_shards) | |
| for index in range(n_shards) | |
| ] | |
| train_downloaded_files = dl_manager.download(data_urls["train"]) | |
| validation_downloaded_files = dl_manager.download(data_urls["validation"]) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files} | |
| ), | |
| ] | |
| def _generate_examples(self, filepaths): | |
| """This function returns the examples in the raw (text) form by iterating on all the files.""" | |
| id_ = 0 | |
| for filepath in filepaths: | |
| logger.info("generating examples from = %s", filepath) | |
| with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: | |
| for line in f: | |
| if line: | |
| example = json.loads(line) | |
| yield id_, example | |
| id_ += 1 | |